Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal causal effects (PCE) in such settings. However, existing PS methods primarily focus on settings with binary intermediate variables. We propose a novel approach to estimate PCE with continuous intermediate variables in the context of stepped wedge cluster randomized trials (SW-CRTs). Our method leverages the time-varying treatment assignment in SW-CRTs to calibrate sensitivity parameters and identify the PCE under realistic assumptions. We demonstrate the application of our approach using data from a cohort SW-CRT evaluating the effect of a crowdsourcing intervention on HIV testing uptake among men who have sex with men in China, with social norms as a continuous intermediate variable. The proposed methodology expands the scope of PS to accommodate continuous variables and provides a practical tool for causal inference in SW-CRTs.

翻译：在存在中间变量的情况下进行因果推断是许多应用中的一个具有挑战性的问题。主分层（PS）为此类情境下估计主因果效应（PCE）提供了一个框架。然而，现有的PS方法主要关注具有二元中间变量的情境。我们提出了一种新颖的方法，用于在阶梯楔形整群随机试验（SW-CRTs）的背景下，估计具有连续中间变量的PCE。我们的方法利用SW-CRTs中随时间变化的治疗分配来校准敏感性参数，并在现实的假设下识别PCE。我们通过使用一项评估众包干预对中国男男性行为者HIV检测接受度影响的队列SW-CRT数据，以社会规范作为连续中间变量，展示了我们方法的应用。所提出的方法扩展了PS的范围以容纳连续变量，并为SW-CRTs中的因果推断提供了一个实用工具。